Computer Science ›› 2019, Vol. 46 ›› Issue (10): 299-306.doi: 10.11896/jsjkx.180901750

• Graphics,Image & Pattern Recognition • Previous Articles     Next Articles

Parking Anomaly Behavior Recognition Method Based on Key Sentence of Behavior Sequence Features

WANG Hong-nian1, SU Han1,2, LONG Gang1, WANG Yan-fei1, YIN Kuan1   

  1. (School of Computer Science,Sichuan Normal University,Chengdu 610101,China)1
    (Visual Computing and Virtual Reality Key Laboratory of Sichuan Province,Chengdu 610066,China)2
  • Received:2018-09-16 Revised:2018-12-17 Online:2019-10-15 Published:2019-10-21

Abstract: With the development of technology and the popularity of cameras,people’s demands on intelligent video surveillance are increasing.Anomaly behavior recognition is a key part of intelligent monitoring systems and plays an important role in maintaining social security.Aiming at the spatio-temporal feature of video data,this paper proposed a method of characterizing behavior as a key sentence with time series,termed Key Sentence of Behavior Sequence (KSBS),and realized the anomaly behavior recognition in the parking scenes by learning key sentences of behaviors.Firstly,the motion sequence is segmented,the foreground target is extracted,and the Motion Period Curve (MPC) of the foreground target is calculated.Then,according to the motion cycle curve,the MPC and DTW method are used to extract the behavior key frames.Finally,based on the semantic understanding method in the field of natural language proces-sing,the behavior key frames are characterized as a series of behavior key sentence.In light of time series features of key sentences,LSTM,which is expert in dealing with time series data,is used to classify the key statements of behaviors.In order to solve the existing data imbalance problem,GAN is used to expand the training set,thus increasing the sample space and balancing the difference between different types of data.Validation results on CASIA behavior database and self-built behavior database show that the average recognition rate of the proposed method for anomaly behavior is 97%.It is proved that the Key Sentece of Behavior Sequence can better represent the behavior information and the LSTM model is more suitable for learning the patterns behind the time series data,verifying the effectiveness of the proposed method on anomaly behavior recognition in parking scenes.

Key words: Anomaly behavior recognition, Features of deep learning, Dynamic time warping, Generative adversarial networks, Long Short-term memory neural network

CLC Number: 

  • TP391
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